The role of law enforcement and public safety programs is to ensure compliance with the law and to maximize public health and safety with a finite number of resources. In order to improve public safety and prevent crimes, law enforcement agencies have begun analyzing volumes of data from multiple systems, searching for trends to help predict the likelihood of future criminal activity in a particular area during a specified time.
The exploration of criminal incident reports for detecting trends, discovering anomalies and evaluating resource usage is an ever expanding issue for law enforcement agencies. It is no longer efficient for a single analyst to pull files, take notes, form hypotheses and request data from different sources. Further, as budgets shrink and departments scale back, the ability of local law enforcement agencies to effectively analyze the data being collected becomes increasingly strained. As such, tools need to be developed that bring varying data sources into a unified framework assisting analysis and exploration in order to speed the analytical process and ease the burden on local agencies.
Many of these tool development needs are being explored by the emergence of a new scientific field, visual analytics. Visual analytics is the science of analytical reasoning assisted by interactive visual interfaces. However, the raw data relating to law enforcement and public health and safety can be difficult to assess or pull together into a unified picture, let alone to predict future needs. Software-based visual analytic tools have been introduced to provide displays of the symptom data in an intuitive way that may be used to identify problem areas, and may include visual analytic tools. The goal of such tools is to provide an intuitive overview of large amounts of data, preferably with the ability to drill down into the data and/or perform additional statistical analysis on select portions of the data.
In the field of law enforcement and health and safety, a user may view instances of violent crimes in a particular geographical area for various times during the year, noting times of year and/or geographic areas when the violent crimes occurred in seemingly larger numbers. The user may then perform statistical analysis to determine further information about those specific instances. However, those systems typically are not capable of both high true positive rates (precision) and low false positive rates (recall). As such, while packages exist for studying spatial relationships between crime and area demographics, including exploratory spatial data analysis to visualize spatial distributions and suggest clusters and hotspots, data sharing and crime analysis via the web, chloropleth mapping, and capability to export records to Excel, it would be appreciated if statistical tools and dynamically linked views allowed for better predictive models. Further, many systems make it difficult to accurately reflect the geographical location of a high rate violent crime area, which may be bounded by physical landmarks such as rivers, buildings, mountains, etc.
Accordingly, there is a need for improved techniques of generating and displaying visual analytics of law enforcement and public health and safety occurrences, and accurately predicting future occurrences.